Method and apparatus for relating to quality of service in wireless networks

ABSTRACT

A method for adapting quality of service to dynamics of a wireless telecommunications network is provided. The method includes collecting data relating to operation of an element in the network, wherein the collected data comprises radio-frequency (RF) data relating to operation of the network. The method also includes pre-calculating, from the collected data, a dynamic operational characteristic of the network, wherein the pre-calculating includes pre-calculating from the collected data to obtain a geometrical determination based on a geographical location of a wireless communications device within the network. The method further includes making available the pre-calculated characteristic of the network to an application of the device using the network, including storing the characteristic at a pre-calculation server accessible by the device so the device can selectively retrieve the characteristic, and updating the characteristic at a frequency based on a trigger related to volatility of the RF data being collected.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.13/600,396, filed Aug. 31, 2012, now U.S. Pat. No. 9,148,490, which is acontinuation of U.S. application Ser. No. 12/876,548, filed Sep. 7,2010, now U.S. Pat. No. 8,270,315, which is a continuation of U.S.application Ser. No. 11/957,905, filed Dec. 17, 2007, now U.S. Pat. No.7,792,051, the entireties of which are herein incorporated by reference.

BACKGROUND

This invention relates in general to quality of service intelecommunications networks, and relates in particular to improving thequality of service in wireless networks.

Users of telecommunications networks expect to receive dependable andreliable levels of service of various kinds of communications on thosenetworks. The term “quality of service” (QoS) is sometimes used as ameasure of quality provided by a particular telecommunications network,and those skilled in the art will recognize that quality of service mayrefer to mechanisms for reserving or allocating hardware orsoftware/logical resources for traffic flowing on a telecommunicationsnetwork. The nature of those resources may depend on user variables,such as the nature of the traffic (for example, video teleconference)and the extent to which the particular kind of traffic may require atleast a minimum level of quality when traveling across the network.Quality of service guarantees may be important in applications such asreal-time streaming multimedia presentations, interactive applications,or voice over IP (VOIP), as those applications often require a fixed byrate and are delay sensitive.

For wireline telecommunications networks, quality of service can besuccessfully provided by building network capacity that exceedspredictable or maximum-expected traffic loads or by building a lessernetwork capacity and utilizing certain QoS mechanisms such as explicitresource reservation (e.g., IntServ, Integrated Services) or trafficmarking with priority traffic treatment (e.g., DiffServ, DifferentiatedServices). The performance of such wireline networks is predictablerelatively independently of events happening outside the network, unlesssuch events result in physical damage to the network itself. Theassumption is that the telecommunications network supports theapplications using the wireline network, and that the network must bedesigned and operated to provide certain levels of quality of servicerequired for the particular applications using the network.

Wireless networks, which rely on radio-frequency (RF) communicationsbetween one or more wireless devices and/or fixed radio sites, cannotprovide a certain and predictable quality of service like wirelinenetworks. Wireless connections are subject to external interference fromunrelated transmissions, co-interference (e.g., from other mobileterminals, or other cells), multi-path changes in signal propagation,signal path changes due to movement of the wireless terminal, and othervariable factors. These factors and changes are largely uncontrollable,usually additive, usually severe, and usually rapid. Traditional qualityof service methods cannot hope to deal reliably with the changesinherent with wireless networks, or at least not at anyeconomically-feasible level.

The difficulties in adapting QoS-sensitive applications to wirelessnetwork dynamics depends on the RF interface, where “RF interface”refers to the portion of the overall network that is truly wireless aswell as the associated equipment and software, the “interface”terminology referring to the transition from the service provider'swireline network portion to the radio or wireless network portion. Agiven quality of service usually requires a particular bandwidth, aswell as possibly a minimum delay and/or minimum jitter, but bandwidth onany given wireless connection cannot be guaranteed because maximumavailable bandwidth is related to the received power of the RF signal.Received RF power varies greatly, and often rapidly, and oftenuncontrollably, because of the above-mentioned factors including changesin RF path, RF channel, and motion- or time-dependent multi-pathvariations in signal travel. These large-magnitude and rapid changes inwireless networks, at the worst, cause the network to drop communicationthat the RF interface cannot support. That result is, of course,unsatisfactory to users of the wireless network and limits thesuccessful adaptation of new applications that require a dependablequality of service for operating on a wireless network, especially wherethese new application and services are particularly sensitive tobandwidth, delay/latency, and/or jitter.

One approach to overcoming the foregoing problem is to modify the RFinterface to be more stable. Such modification, in traditional practice,amounts to adding more cell sites to the RF network so as to increasethe breadth and the depth of coverage for the RF network. That approachis extremely expensive and, furthermore, may present practical as wellas political problems in obtaining approval to add cell towers in someareas. Moreover, even adding cell sites to a near-saturation level maynot succeed in overcoming the problems inherent with RF signalpropagation.

Another possible solution to the problem is doing something in thewireless network to partially compensate for the uncontrollablevariations of the RF interface. However, the wireless network cannotgenerally accomplish this because the variations inherent in the RFinterface are mostly uncontrollable, such that the variable aspects ofthe wireless network cannot be commanded or controlled to adapt orcontrol other elements in the data network.

SUMMARY

Stated in general terms, embodiments of the present method and apparatusenable an application to adapt to or compensate for changes in operationof a wireless network, instead of attempting to command the wirelessnetwork to adapt to the application. Embodiments accomplish suchadaptation by collecting pertinent RF data concerning operation of thewireless network, and pre-calculating at least one anticipatedoperational characteristic of the wireless network based on one or moreoperational network dynamics determined from the pre-calculation. Thepre-calculated operational characteristic of the wireless network isprovided to the application, so that the operation of the applicationcan adapt to the anticipated dynamic variation in operation of thewireless network.

In one embodiment of the disclosure, a method for adapting quality ofservice to dynamics of a wireless telecommunications network isprovided. The method includes collecting data relating to operation ofan element in the network, wherein the collected data comprisesradio-frequency (RF) data relating to operation of the network. Themethod also includes pre-calculating, from the collected data, a dynamicoperational characteristic of the network, wherein the pre-calculatingincludes pre-calculating from the collected data to obtain a geometricaldetermination based on a geographical location of a wirelesscommunications device within the network. The method further includesmaking available the pre-calculated characteristic of the network to anapplication of the device using the network, including storing thecharacteristic at a pre-calculation server accessible by the device sothe device can selectively retrieve the characteristic, and updating thecharacteristic at a frequency based on a trigger related to volatilityof the RF data being collected.

BRIEF DESCRIPTION OF DISCLOSED EMBODIMENT

FIG. 1 is a block diagram depicting a method of operating an applicationaccording to an exemplary embodiment.

FIG. 2 is a block diagram showing an embodiment of apparatus foradapting an application to quality of service dynamics of a wirelessnetwork.

FIGS. 3 and 3A depict an application of the embodiment of FIG. 1 to anIP multimedia subsystem (IMS).

DETAILED DESCRIPTION OF DISCLOSED EMBODIMENT

FIG. 1 illustrates elements that enable the adaptation of applicationsusing a wireless network, according to an exemplary embodiment. Thefirst element of the illustrative embodiment is data collection, at 10in FIG. 1. Data collection comprises measurements of selected variableinformation relating to operation of a wireless network, or to operationof wireless portions of a network, and is intended to find out what ishappening in the wireless network. Although operating parameters of atypical wireless network may change for a variety of reasons, measureddata according to the present embodiment generally comprises measuringthe signal power of RF signals received at various places in thenetwork, shown in FIG. 1, and measuring RF noise power levels at variousplaces on the wireless network as shown at 20. Measurements of signalpower and noise power may be made at one or more base stations in thewireless network, e.g., at cell towers, or at a user's cell phone orother remote terminal using the wireless network at locations relevantto the operation of a particular application on that network. Themeasured RF signal power and RF noise power permit calculatingsignal/noise ratios (SNRs), as those skilled in the art can appreciate.Data delay/latency and/or data jitter may also be measured at pointswithin or related to the wireless network, even though they are notspecifically wireless network quantities, since they can help measureQoS, and may be used in conjunction with measured signal/noise powersand SNRs, especially for applications/services which are particularlysensitive to delay/latency and/or jitter.

Data collection 10 also includes keeping track of the history of thereceived signal and noise levels, and may also include keeping track ofthe history of any other measured quantities such as delay/latencyand/or jitter, in order to determine trends, for example, if signalpower is increasing or decreasing at a certain rate, at certainlocations, at a certain time of day on a certain day of the week, and soon. Such trends relating to time-of-day or day-of-week, if measured forparticular users of a wireless network, may be useful for anticipatingdynamic variations in the operation of the wireless network based on orcorrelated to activities of a particular user or group of users.

The amount of data that is collected during the data collection stage 10may include signal/noise data for each cell tower or cell site in awireless network, and for many if not most users of the wirelessnetwork. Associated delay/latency and/or jitter may also be measured.Furthermore, the data may be collected at relatively short intervals oftime, so that the data will reflect signal/noise variations that mayoccur over milliseconds, seconds, and hours of each day and days of theweek (e.g., weekdays vs. weekends). The volume of such data collectedfor a wireless network will thus become too great for most applicationsto utilize on anything approaching a real-time usage of the wirelessnetwork, and could lead to enormous processing demands that would beoverlapping and often replicated between various applications andservices. The pre-calculation stage 10 seeks to assimilate large volumesof raw data obtained during the data collection stage, and to obtain oneor more measures usable to anticipate or predict near-term futureperformance of the wireless network. Such a performance measure ormetric is then supplied to a particular application using the network,so that the application can adapt to the predicted performance of thewireless network according to the measure or measures thus obtained fromthe data collection. Different metrics on different time scales may beuseful for different types of applications and services, while similarmetrics on similar time scales may be useful for similar types ofapplications and services, such that the pre-calculation may be arrangedto provide a set of the most useful metrics on a variety of time scalesin order to efficiently and effectively serve the needs of most if notall application services.

One form of pre-calculation involves averaging or other statisticalanalyses, as indicated at 22 in FIG. 1, of the collected data in aneffort to obtain probabilistic measures of wireless-network performanceover various time intervals. Such analysis of the collected data mayutilize any statistical analysis, e.g., averaging and standarddeviations over a predetermined scale that would indicate the relativestability of the signal/noise data or the like. That data analysis maybe performed with data taken over various time intervals, so as tosupport estimation of wireless-network performance during thoseintervals and also to support predicted performance of the wirelessnetwork over some near-term future time during which an application willutilize that network. For example, if the collected data indicates thatthe wireless network is likely to be relatively unstable at a particulartime of day, an application using the network during that time may setits resolution or another operating parameter lower than thatapplication normally would, so as to adapt in a conservative way to thepredicted unstable operation of the wireless network supporting use bythe application.

Averaging and other statistical analyses, e.g., over various timeintervals, may also be applied to other pre-calculated metrics asdisclosed herein or as otherwise adopted and used according toembodiments of the disclosed method and apparatus. For a given metric,data analysis may be done using intervals of multiple lengths, sincedifferent applications and services may need to use different resultsreflecting different lengths of time intervals even for the same metric.

Pre-calculation may also include geometrical or map-based functions asindicated at 24 in FIG. 1. Such considerations are, generally speaking,based on locations of wireless devices relative to cell sites within awireless network. Those relative locations may be expressed in terms ofrange, for example, within a certain distance from the particular cellsite, or based on latitude-longitude coordinates of a particularwireless device. Latitude-longitude coordinates for wireless devices maybe obtained, for example, by a GPS capability incorporated in thewireless device, or by other techniques known to those skilled in theart. Furthermore, so-called geographic information systems (GIS) thatcontain information including map-based and/or map-related informationsuch as on geographic or land-based structures are also known in theart, for example, including buildings or other structures that may havean adverse effect on RF propagation to or from wireless devices inproximity to the structure.

The location of a wireless device, whether map-based or determinedwithin a certain radial/angular relation to a cell site, may correlatewith RF signal/noise levels determined during the data collection phase.Those correlations also may exist for instances where the wirelessdevice has predetermined locations or is moving with respect to buildingor other structures, that may affect signal/noise levels due tocharacteristics such as attenuation or reflection of RF signals betweenthe wireless device and one or more cell sites. Those correlations withthe collected data may also vary during the day, e.g., due to relativedensity of traffic over the wireless network at certain times of the dayand days of the week. Such determinations lead to and enable trendprojection as at 26 in FIG. 1, to help provide an application with anidea of what near-term future performance to expect from a wirelessnetwork or portion of a wireless network that an application is about touse, not just how the wireless network has performed at a particulartime, location of the wireless device, or other factor included in thedata collection phase.

Trend projection based on the collected data also enables comparison onmultiple related or potentially-interacting trends. For example,wireless traffic for users traveling along a particular location, suchas an expressway, may peak at various times of day when traffic jams arelikely to occur. However, weekend travelers along the same expresswaymay be less likely to encounter traffic jams and thus less likely toutilize wireless services at the same time, thereby producing lessinterference on the available wireless network. Applications using thewireless network can thus benefit from pre-calculated data based onlocation of the wireless user, and on date/time data, when producing ametric for adjusting operation of an application according to projectedcapability of the wireless network.

Pre-calculation for historical comparison and estimation, shown at 28 inFIG. 1, is related to trend projection. Historical comparison enableshistorically-repeatable aspects of a wireless network, as reflected inthe data collection, to be captured and utilized. As previouslymentioned, time-of-day and weekend vs. weekday are two examples ofnetwork performance that may be historically repeatable and utilized tohelp provide an application with an idea of what quality of service toexpect of the wireless network at a particular time.

Determinations of stability or instability, shown at 30 in FIG. 1, maybe considered as measures of how dynamic the particular results,summarized or obtained through techniques such as averaging,geometric/map-based correlation, and so on, have been and/or are likelyto be in view of the data collected. Such determination and/orprediction may be made in terms of a particular time window or multipletime windows of different lengths or sizes. If the RF signal/noisemeasurements are relatively volatile, subject to frequent fluctuationswithout any measurable pattern or trend, the wireless network may beconsidered relatively unstable, for example leading to any expectationthat a given QoS-related metric will likely decrease below a givenreasonable threshold with a frequency greater than a threshold frequencyacceptable to the application, and an application using that networkshould be operated accordingly. For example, an application might be setto provide resolution lower than that application normally wouldprovide, so as to adapt in a relatively conservative way to the expectedless-reliable or less-consistent service quality of the wirelessnetwork, as opposed to expecting the best of the wireless network ofjust assuming that performance of the wireless network would stay thesame over the expected time that the application will utilize thatnetwork or that portion of the network.

Reverting to the discussion of the statistical analysis with respect to22 in FIG. 1, the standard deviation of projected capability for arelatively volatile or unstable wireless network might be categorizeddifferently from the standard deviation expected from a relativelystable wireless network. Those pre-calculated standard deviation valuescould then be used to adapt the operation of a particular applicationusing that wireless network.

The results of the pre-calculations may be placed on a server or aninterface, or otherwise where an application can obtain thepre-calculation information upon request. The RF status information, onwhich the pre-calculations are based, is preferably updated more or lesscontinuously as indicated at 14 in FIG. 1, and the pre-calculation maylikewise be updated based on the ongoing input from the data collection10. The frequency at which the pre-calculations are updated foravailability may be performed and updated at a slightly lower rate ofoccurrence, but that updated rate could also change based on thevolatility of the RF data information being obtained, for instance asvolatility increases the update rate could be accordingly increased.Because the goal is being able to provide the applications withmeaningful current information upon request, if the RF data beingcollected is relatively stable, the frequency at which the factors arepre-calculated and updated at 14 may be reduced or performedperiodically so as to conserve traffic in the overall system. Differentmetrics or factors may be pre-calculated and updated at differentfrequencies.

The data being collected at 10 will likely come from mobile wirelessdevices such as cell phones and other devices currently communicatingwith the wireless network. However, information from other sources, suchas sensors located at various particular points within the wirelessnetwork, may also provide input to the data collection phase. Thepre-calculation may take place either at a central processor asdescribed below or dispersed among plural processors, with the resultsof pre-calculation being made available through at least one centralinterface or other logical access point where all relevant applicationscan access for information relevant to the operation or requirements ofdifferent applications. For example, some applications may not careabout trend projection because those applications cannot usefully orpractically extend or adapt operation into the future beyond real-time,some types of video teleconferencing being one such example. Otherapplications may care very much about trend projection, but not so muchabout geometrical or map-based pre-calculation functions. Based on theparticular service quality requirements for individual applications,those applications can look to a common data source for pre-calculationinformation most relevant to those requirements. That pre-calculationinformation in effect tells the application the maximum quality ofservice available from the wireless network at the time, together withany trend projection or other relevant information based on thepre-calculations. Given that available information about bandwidthcapabilities and possible other metrics currently available from thewireless network, various applications may be able to adapt theiroperations to best conform with that available bandwidth and othermetrics and the quality of service supportable by that bandwidth andother metrics.

FIG. 2 illustrates, in functional form, an embodiment of apparatus forproviding quality of service adaptation to wireless network dynamics.The wireless network indicated generally at 40 will be understood bythose skilled in the art to include all the structural and functionalelements making up such networks. These elements include cell phones andother wireless devices, as well as cell sites/base stations,controllers, and other hardware and software elements required foroperation of a typical wireless network. The wireless network 40connects to a telecom network shown generally at 42, which may includethe conventional public switched telephone network and packet-switchednetworks and which enables users of the wireless network 40 tocommunicate with other telecommunications users outside of that wirelessnetwork.

The data collection phase 10, discussed above and shown in FIG. 1, takesplace within wireless network 40 in the disclosed embodiment, as thatwireless network is the primary source of data relating to measureablelevels of RF signal and RF noise for wireless communications beinghandled by the wireless network 40. The data collected by the wirelessnetwork 40 is provided to pre-calculation processor 44, for calculatingthe statistical and other analyses such as described above with respectto the pre-calculation phase 12. The data provided to pre-calculationprocessor 44 may include geometrical and/or map-based informationrelating to the particular sources of the collected data, as mentionedabove. The metrics resulting from the pre-calculation processor 44,including periodic or triggered updating as indicated at 14 and asdiscussed above, are supplied to the pre-calculation server 46, wherethose pre-calculation metrics are made available to any application thatuses or is about to use the wireless network 40. For example, userapplications 48 may operate over the wireless network 40, either througha direct link 50 with that wireless network or through thetelecommunications network 42 as indicated by the link 52. In eithercase, a particular user application 48, when activated for use on thewireless network 40, may query the pre-calculation server 46 asindicated by the link 54 for the pre-calculated information relating tothe quality of service presently available for the particular user onthat wireless network. That available quality of service typically isdetermined by the maximum bandwidth and possible other metrics that thewireless network 40 can support at the time of request by theapplication, as well as information such as trend projection, relativestability/instability of the wireless network, and other pre-calculatedfactors based on data being supplied by the wireless network 40. Basedon the maximum projected quality of service that the wireless network 40can currently support for the particular user, the particular userapplication may adapt its operation as appropriate for that quality ofservice. For example, some application such as data transmission cantolerate a relatively low transmission rate occasioned bycorrespondingly-low available quality of service available from thewireless network 40, while other applications such as voice or video maybe less tolerant of delays and/or jitter including such as may beoccasioned by a momentary or projected reduction in available quality ofservice. In such instances the user application might inform the userthat relatively high-bandwidth service over the particular wirelessnetwork is not currently available, or may retry the wireless networkafter a brief delay, instead of connecting the user to the wirelessnetwork without regard for the actual quality of service currentlyavailable and/or predicted to be available from that network.

The pre-calculation processor 44 and the pre-calculation server 46,although depicted in FIG. 2 as separate from the wireless network 40,may be provided by the operator of the wireless network as a servicemade available for applications 48 to access when using that wirelessnetwork. In that way, the operator of the wireless network 40 can enableusers to adjust their own applications for adapting to wireless networkdynamics over which the wireless network 40 has little or no effectivecontrol. Moreover, and as mentioned above, the pre-calculation processor44, using data collected at the wireless network 40, may be locatedeither centrally or may distributed or shared among plural processorshaving access to the collected data 10, and the pre-calculation server46 likewise may be either located centrally or at several locationsdistributed throughout the wireless network or elsewhere as appropriate.

FIGS. 3 and 3A are a functional block diagram showing an example ofimplementing the disclosed embodiment into an IP Multimedia Subsystem(IMS) for wireless communication. IMS is an architectural framework fordelivering internet protocol (IP) multimedia to mobile users. The natureand operation of IMS is well documented and understood by those skilledin the art, and for that reason the individual elements of typical IMSarchitecture are omitted from FIGS. 3 and 3A. However, those figures doshow three basic functional layers of IMS, namely, a serviceapplications layer 300, an IMS layer 310, and a transport layer 320(FIG. 3A). Generally speaking, the service/applications layer 300includes a master user database that supports the IMS network entitiesthat actually handle calls over the wireless network. The IMS layer 310may be considered a kind of middleware layer that handles signaling andservice queries to or from the wireless network. The transport layer 320handles actual access to and from users of the wireless network andinterconnections between that network and the greater telecommunicationsnetwork as illustrated at 42 in FIG. 2. Those skilled in the art willrealize that the functions of IMS may be implemented at differing levelsin a single network and also may be present multiple times in a singlenetwork for load balancing or organizational issues.

FIGS. 3 and 3A are intended to depict an embodiment for implementing, inan exemplary IMS framework, the functions shown and described withregard to FIG. 1. Thus, the radio data collection function (RDCF) 10,which collects the data obtained from the operation of the wirelessdevices operating within the wireless network, appears within thetransport layer 320. That radio data may include GPS or otherinformation collected from the wireless devices, as well as signal/noiselevels and other relevant information obtained from cell sites and/orwireless devices. That data is collected and forwarded to the radiocalculation function (RCF) 12, shown in FIG. 3 as within the IMS layer310. Information pre-calculated according to the radio calculationfunction 12 may be furnished to the radio status update function (RSUF)14, shown in FIG. 3 as part of the service applications layers 300. Theapplications 48 themselves, shown logically as part of theservice/applications layer 300, would have a radio status query function(RSQF) 350 which operates to query the radio status update function 14.The applications 48 also include an appropriate adaptation function, aspreviously mentioned and shown in FIG. 3A as the radio status adaptfunction (RSAF) 360, that enables the application 48 to do anappropriate adaptation controlled by or in response to the radio statusupdate function, e.g., adjusting the mode and/or event delays and/orparameters such as resolution and/or coding and/or transmission rate ofdata to be communicated over the wireless network, thereby adapting theapplication to the quality of service presently or prospectivelyavailable for that application on the wireless network.

Although IMS is discussed as an architectural framework for theembodiment disclosed with respect to FIGS. 3 and 3A, it should beunderstood that other middleware and/or enabling systems may be modifiedand used to support embodiments of the method and apparatus disclosedherein.

Furthermore, it should be understood that the foregoing relates only todisclosed embodiments of the present invention, and that changes andmodifications thereto may be made without departing from the spirit andscope of the invention as set forth in the following claims.

The invention claimed is:
 1. A method for allowing an application of awireless communications device to adapt to a dynamic of a wirelesstelecommunications network, the method comprising: determining, by aprocessor, in response to a trigger associated with a frequency ofoperation of an element in the wireless telecommunications network,using data comprising radio-frequency information relating to operationof the element of the wireless telecommunications network, a dynamicoperational characteristic of the wireless telecommunications network;adjusting the application based on the dynamic operationalcharacteristic; updating the dynamic operational characteristic at atiming based on a volatility of the radio-frequency information; anddetermining, based on the volatility of the radio-frequency information,a quality of service metric for use in the wireless telecommunicationsnetwork for the wireless communications device.
 2. The method of claim1, further comprising making the dynamic operational characteristicavailable to the application of the wireless communications device,wherein making the dynamic operational characteristic available to theapplication of the wireless communications device comprises storing thedynamic operational characteristic at a server accessible by thewireless communications device so that the wireless communicationsdevice can selectively retrieve the dynamic operational characteristicfrom the server.
 3. The method of claim 1, wherein the data furthercomprises location information associated with the element of thewireless telecommunications network.
 4. The method of claim 3, whereinthe location information includes at least one piece of informationselected from a group consisting of map-based information and geometricinformation.
 5. The method of claim 1, wherein determining the dynamicoperational characteristic comprises geometric/map-based correlations.6. The method of claim 1, wherein the dynamic operational characteristiccomprises an anticipated dynamic variation in the wirelesstelecommunications network.
 7. The method of claim 1, whereindetermining the dynamic operational characteristic comprises determiningthe dynamic operational characteristic based on a location of thewireless communications device.
 8. The method of claim 1, wherein thedynamic operational characteristic indicates a probabilisticdetermination of future dynamic operation of the wirelesstelecommunications network.
 9. The method of claim 1, wherein thedynamic operational characteristic indicates a projected trend for useby the application of the wireless communications device in advance of acondition anticipated by the projected trend.
 10. The method of claim 1,wherein the dynamic operational characteristic indicates a historicalcomparison for use by the application of the wireless communicationsdevice to adapt operation of the application according to ahistorically-repeatable quality, of the wireless telecommunicationsnetwork, indicated by the historical comparison.
 11. The method of claim1, wherein the dynamic operational characteristic indicates a measureindicating stability of the wireless telecommunications network.
 12. Themethod of claim 1, wherein the dynamic operational characteristicindicates multiple modes for use by the application of the wirelesscommunications device to select an appropriate mode of the multiplemodes.
 13. A non-transitory computer-readable storage device havingstored thereon computer-executable instructions that, when executed by aprocessor, cause the processor to perform operations for allowing anapplication of a wireless communications device to adapt to a dynamic ofa wireless telecommunications network, the operations comprising:determining, in response to a trigger associated with a frequency ofoperation of an element in the wireless telecommunications network,using data comprising radio-frequency information relating to operationof the element of the wireless telecommunications network, a dynamicoperational characteristic of the wireless telecommunications network;adjusting the application based on the dynamic operationalcharacteristic; updating the dynamic operational characteristic at atiming based on a volatility of the radio-frequency information; anddetermining, based on the volatility of the radio-frequency information,a quality of service metric for use in the wireless telecommunicationsnetwork for the wireless communications device.
 14. The non-transitorycomputer-readable storage device of claim 13, wherein the dynamicoperational characteristic comprises an anticipated dynamic variation inthe wireless telecommunications network.
 15. The non-transitorycomputer-readable storage device of claim 13, wherein the dynamicoperational characteristic comprises a probabilistic determination offuture dynamic operation of the wireless telecommunications network. 16.The non-transitory computer-readable storage device of claim 13, whereinthe dynamic operational characteristic comprises a projected trend foruse by the application of the wireless communications device in advanceof a condition anticipated by the projected trend.
 17. Thenon-transitory computer-readable storage device of claim 13, wherein thedynamic operational characteristic comprises a historical comparison foruse by the application of the wireless communications device to adaptoperation of the application according to a historically-repeatablequality, of the wireless telecommunications network, indicated by thehistorical comparison.
 18. The non-transitory computer-readable storagedevice of claim 13, wherein the dynamic operational characteristiccomprises a measure indicating stability of the wirelesstelecommunications network.
 19. The non-transitory computer-readablestorage device of claim 13, wherein determining the dynamic operationalcharacteristic is based on a location of the wireless communicationsdevice.
 20. A system for allowing an application of a wirelesscommunications device to adapt to a dynamic of a wirelesstelecommunications network, the system comprising: a processor; and acomputer-readable storage device that stores computer-executableinstructions that, when executed by the processor, cause the processorto perform operations comprising: determining, in response to a triggerassociated with a frequency of operation of an element in the wirelesstelecommunications network, using data comprising radio-frequencyinformation relating to operation of the element of the wirelesstelecommunications network, a dynamic operational characteristic of thewireless telecommunications network; adjusting the application based onthe dynamic operational characteristic; updating the dynamic operationalcharacteristic at a timing based on a volatility of the radio-frequencyinformation; and determining, based on the volatility of theradio-frequency information, a quality of service metric for use in thewireless telecommunications network for the wireless communicationsdevice.